Hive is a database technology which will define databases and tables to analyze structured data. The theme for structured data analysis is to store the data in a tabular manner, and pass queries to analyze it. This blog explains how to create Hive database. Hive contains a default database named default.

Create Database Statement

Create Database is a statement employed to create a database in Hive. A database in Hive is a namespace or a collection of tables. The syntax for this statement is as follows:

CREATE DATABASE|SCHEMA [IF NOT EXISTS] <database name>

Here, IF NOT EXISTS is an optional clause, which notifies the user that a database with the same name already exists. We can use SCHEMA in place of DATABASE in this command. The subsequent query is executed to create a database named userdb:

hive> CREATE DATABASE [IF NOT EXISTS] userdb;

hive> CREATE SCHEMA userdb;

The subsequent query is employed to verify a databases list:

hive> SHOW DATABASES;

default

userdb

JDBC Program

The JDBC program to create a database is given below.

import java.sql.SQLException;

import java.sql.Connection;

import java.sql.ResultSet; 4. CREATE DATABASE

import java.sql.Statement;

import java.sql.DriverManager;

public class HiveCreateDb {

private static String driverName =

“org.apache.hadoop.hive.jdbc.HiveDriver”;

public static void main(String[] args) throws SQLException {

// Register driver and create driver instance

Class.forName(driverName);

// get connection

Connection con = DriverManager.

getConnection(“jdbc:hive://localhost:10000/default”, “”, “”);

Statement stmt = con.createStatement();

stmt.executeQuery(“CREATE DATABASE userdb”);

System.out.println(“Database userdb created successfully.”);

con.close();

}

Save the program in a file named HiveCreateDb.java. The subsequent commands are employed to compile and execute this program.

$ javac HiveCreateDb.java

$ java HiveCreateDb

Output:

Database userdb created successfully.

Hive – Drop Database

This section describes how to drop a database in Hive. The usage of SCHEMA and DATABASE are same.

Drop Database Statement

Drop Database is a statement that drops all the tables and deletes the database. Its syntax is as follows:

DROP DATABASE StatementDROP (DATABASE|SCHEMA) [IF EXISTS] database_name

[RESTRICT|CASCADE];

The subsequent queries are employed to drop a database. Let us assume that the database name is userdb.

hive> DROP DATABASE IF EXISTS userdb;

The subsequent query drops the database using CASCADE. It means dropping respective tables before dropping the database.

hive> DROP DATABASE IF EXISTS userdb CASCADE;

The subsequent query drops the database using SCHEMA.

hive> DROP SCHEMA userdb;

This clause was added in Hive 0.6.

JDBC Program

The JDBC program to drop a database is given below.

import java.sql.SQLException;

import java.sql.Connection;

import java.sql.ResultSet;

import java.sql.Statement;

import java.sql.DriverManager; 5. DROP DATABASE

public class HiveDropDb {

private static String driverName =

“org.apache.hadoop.hive.jdbc.HiveDriver”;

public static void main(String[] args) throws SQLException {

// Register driver and create driver instance

Class.forName(driverName);

// get connection

Connection con = DriverManager.

getConnection(“jdbc:hive://localhost:10000/default”, “”, “”);

Statement stmt = con.createStatement();

stmt.executeQuery(“DROP DATABASE userdb”);

System.out.println(“Drop userdb database successful.”);

con.close();

}

Save the program in a file named HiveDropDb.java. Given below are the commands to compile and execute this program.

$ javac HiveDropDb.java

$ java HiveDropDb

Output:

Drop userdb database successful.

So, this brings us to the end of blog. This Tecklearn ‘How to create and drop a database in Hive’ helps you with commonly asked questions if you are looking out for a job in Big Data and Hadoop Domain.

If you wish to learn Hive and build a career in Big Data or Hadoop domain, then check out our interactive, Big Data Hadoop-Architect (All in 1) Combo Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Why Hadoop Security Is Important
Hadoop’s Security System Concepts
What Kerberos Is and How it Works
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Cluster Maintenance

Checking HDFS Status
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Cluster Monitoring and Troubleshooting

General System Monitoring
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Common Troubleshooting Hadoop Clusters
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Introduction to Pig

What Is Pig?
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Basic Data Analysis with Pig

Pig Latin Syntax
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Field Definitions
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Commonly-Used Functions

Processing Complex Data with Pig

Storage Formats
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Multi-Dataset Operations with Pig

Techniques for Combining Data Sets
Joining Data Sets in Pig
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Troubleshooting Pig
Logging
Using Hadoop’s Web UI
Data Sampling and Debugging
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Introduction to Hive and Impala

What Is Hive?
What Is Impala?
Schema and Data Storage
Comparing Hive to Traditional Databases
Hive Use Cases

Querying with Hive and Impala

Databases and Tables
Basic Hive and Impala Query Language Syntax
Data Types
Differences Between Hive and Impala Query Syntax
Using Hue to Execute Queries
Using the Impala Shell

Data Management

Data Storage
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Altering Databases and Tables
Simplifying Queries with Views
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Data Storage and Performance

Partitioning Tables
Choosing a File Format
Managing Metadata
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Relational Data Analysis with Hive and Impala

Joining Datasets
Common Built-In Functions
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Working with Impala

How Impala Executes Queries
Extending Impala with User-Defined Functions
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Analyzing Text and Complex Data with Hive

Complex Values in Hive
Using Regular Expressions in Hive
Sentiment Analysis and N-Grams
Conclusion

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Understanding Query Performance
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Bucketing
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Extending Hive

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User-Defined Functions
Parameterized Queries

Importing Relational Data with Apache Sqoop

Sqoop Overview
Basic Imports and Exports
Limiting Results
Improving Sqoop’s Performance
Sqoop 2

Introduction to Impala and Hive

Introduction to Impala and Hive
Why Use Impala and Hive?
Comparing Hive to Traditional Databases
Hive Use Cases

Modelling and Managing Data with Impala and Hive

Data Storage Overview
Creating Databases and Tables
Loading Data into Tables
HCatalog
Impala Metadata Caching

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Selecting a File Format
Hadoop Tool Support for File Formats
Avro Schemas
Using Avro with Hive and Sqoop
Avro Schema Evolution
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Data Partitioning

Partitioning Overview
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What is Apache Flume?
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Flume Sources
Flume Sinks
Flume Channels
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Spark Basics

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Using the Spark Shell
RDDs (Resilient Distributed Datasets)
Functional Programming in Spark

Working with RDDs in Spark

A Closer Look at RDDs
Key-Value Pair RDDs
MapReduce
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Spark Applications vs. Spark Shell
Creating the SparkContext
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Parallel Programming with Spark

Review: Spark on a Cluster
RDD Partitions
Partitioning of File-based RDDs
HDFS and Data Locality
Executing Parallel Operations
Stages and Tasks

Spark Caching and Persistence

RDD Lineage
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Distributed Persistence

Common Patterns in Spark Data Processing

Common Spark Use Cases
Iterative Algorithms in Spark
Graph Processing and Analysis
Machine Learning
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Spark SQL and the SQL Context
Creating DataFrames
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Saving DataFrames
Comparing Spark SQL with Impala

Hadoop Testing

Hadoop Application Testing
Roles and Responsibilities of Hadoop Testing Professional
Framework MRUnit for Testing of MapReduce Programs
Unit Testing
Test Execution
Test Plan Strategy and Writing Test Cases for Testing Hadoop Application

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Golden Data Set

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Building and Set up
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Hadoop Cluster
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IBM Project

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